Abstract:
Recent advancements in small unmanned aerial systems (sUAS) have proved
useful in monitoring and counting aquatic birds in wetlands flying over flocks
without disturbing them. The objective of the study is to use a semi-automated
workflow to extract waterfowl species and counts from a managed wetland in
Colusa County, California. Over 560 UAV images were obtained using a DJI
Mavic 2 PRO in a series of parallel flight lines at an average Ground Sample
Distance (GSD) of approximately 3 cm/px. A rule-based feature extraction
workflow in ENVI was used to extract waterfowl objects, using the Edge algorithm
at a scale of 75% and the Full Schedule Lambda Merge algorithm at a level of
95%. An extent of waterfowl presence (6.8 ha) and waterfowl absence (1.4 ha)
imagery was used for object-based image analysis (OBIA) and we counted
approximately 2,259 birds. The overall classification accuracy for identifying birds
was 57.3%. The user's accuracy for birds and non-birds was 93.9% and 51.5% and
the producer’s accuracy for birds and non-birds was 23.6% and 98.1% respectively.
The unique characteristics of our study site present challenges for conducting bird
counts, which may require conducting both automated and manual counts in
defined subsets of habitat.
